Short Term Electric Load Forecasting Based on Data Transformation and Statistical Machine Learning
| Autor: | Sophia Daskalaki, Christos Valouxis, Alexios Birbas, Aristeidis Magklaras, Michael Birbas, Efthymios Housos, Nikos Andriopoulos, George P. Papaioannou, Alex D. Papalexopoulos |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Electrical load
Computer science 020209 energy Data transformation (statistics) 02 engineering and technology Machine learning computer.software_genre 7. Clean energy lcsh:Technology lcsh:Chemistry statistical analysis short-term electrical load forecasting 0202 electrical engineering electronic engineering information engineering Feature (machine learning) General Materials Science Instrumentation lcsh:QH301-705.5 Fluid Flow and Transfer Processes Artificial neural network business.industry lcsh:T Process Chemistry and Technology Deep learning 020208 electrical & electronic engineering General Engineering deep learning Grid lcsh:QC1-999 Computer Science Applications Term (time) Smart grid machine learning lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 Artificial intelligence business LSTM lcsh:Engineering (General). Civil engineering (General) computer CNN lcsh:Physics parameters tuning |
| Zdroj: | Applied Sciences, Vol 11, Iss 158, p 158 (2021) Applied Sciences Volume 11 Issue 1 |
| ISSN: | 2076-3417 |
| Popis: | The continuous penetration of renewable energy resources (RES) into the energy mix and the transition of the traditional electric grid towards a more intelligent, flexible and interactive system, has brought electrical load forecasting to the foreground of smart grid planning and operation. Predicting the electric load is a challenging task due to its high volatility and uncertainty, either when it refers to the distribution system or to a single household. In this paper, a novel methodology is introduced which leverages the advantages of the state-of-the-art deep learning algorithms and specifically the Convolution Neural Nets (CNN). The main feature of the proposed methodology is the exploitation of the statistical properties of each time series dataset, so as to optimize the hyper-parameters of the neural network and in addition transform the given dataset into a form that allows maximum exploitation of the CNN algorithm&rsquo s advantages. The proposed algorithm is compared with the LSTM (Long Short Term Memory) technique which is the state of the art solution for electric load forecasting. The evaluation of the algorithms was conducted by employing three open-source, publicly available datasets. The experimental results show strong evidence of the effectiveness of the proposed methodology. |
| Databáze: | OpenAIRE |
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